Long before the current resurgence of interest in deep learning, there were already neural networks designed specifically to handle CDE , \(e.g.\) 
[1]} adressed the problem of estimating a probability density using Mixture Density Networks (MDN ). More recently, Kernel Mixing Networks (KMN , [2]}) are networks trained to estimate a family of kernels to perform the CDE task. Another famous variational inference technique is Flow Normalization (N.Flow , [3]}), designed to solve the problem of finding the appropriate approximation of the posterior distribution. A common challenge with most of these methods is scalability in terms of computation resources, which our experimental benchmark confirmed. A python implementation of MDN , KMN and N.Flow is provided by the \(freelunchtheorem\)  package ([4]}), which we included in our benchmark.
